The uninterrupted spread of malaria, besides its seasonal uncertainty, is due to the lack of suitable planning and intervention mechanisms and tools. Several studies have been carried out to understand the factors that affect the development and transmission of malaria, but these efforts have been largely limited to piecemeal specific methods, hence they do not offer comprehensive solutions to predict disease outbreaks. This thesis introduces a ’holistic’ approach to understand the relationship between climate parameters and the occurrence of malaria using both mathematical and computational methods. In this respect, we develop new climate-based models using mathematical, agent-based and data-driven modelling techniques. A malaria model is developed using mathematical modelling to investigate the impact of temperature-dependent delays. Although this method is widely applicable, but it is limited to the study of homogeneous populations. An agent-based technique is employed to address this limitation, where the spatial and temporal variability of agents involved in the transmission of malaria are taken into account. Moreover, whilst the mathematical and agent-based approaches allow for temperature and precipitation in the modelling process, they do not capture other dynamics that might potentially affect malaria. Hence, to accommodate the climatic predictors of malaria, an intelligent predictive model is developed using machine-learning algorithms, which supports predictions of endemics in certain geographical areas by monitoring the risk factors, e.g., temperature and humidity. The thesis not only synthesises mathematical and computational methods to better understand the disease dynamics and its transmission, but also provides healthcare providers and policy makers with better planning and intervention tools.

No description available.

No description available.

<p>Sustainable food production is about producing more and better with less.As an emerging CEA system, aquaponics integrates recirculating aquaculture systems and hydroponics and can achieve the three SDGs mentioned above. However, challenges in sustainable aquaponics commercialization remains and my thesis addresses the following three layers of sustainable aquaponics  development:  sustainability  assessment,  sustainable  system  design  and management, understanding biological mechanisms for scalability.</p> <p>I conducted acradle-to-gate life cycle assessment (LCA)and compared the environmental performance, on an economic basis, of aquaponics andhydroponics withidentical system design in Indiana, US. Aquaponics produced 45% lower endpoint environmental impact than hydroponics.Electricity use for greenhouse heating and lighting, and water pumping and heating contributed to themajority of the environmental impacts of both systems, which was followed by the production of fishfeed and fertilizers. However, changing the energy source from coal to wind power could make thehydroponic system more environment-friendly than the aquaponic system. This LCA study can provideCEA farmers with the groundwork to reduce the environmental cost of their production.</p> <p>Aquaponics uses bacterial processes and plant nutrient uptake to recover nutrient from aquaculture wastewater. However, little is known which wastewater management strategy, autotrophic or heterotrophic, is best suited for the four objectives: nutrient recovery, system reliability, and growth and physiological welfare of fish and plants. In this study, I found that pH6 had the highest nitrogen (N) use efficiency (NUE) (assimilated by fish and plants, 65.5%) and the lowest N loss as gas (34.5%), followed by pH6M (55.5% and 44.5%,respectively), suggesting that lower pH and less organic carbon in aquaponics could enhance NUE and reduce N loss. pH6M had the highest phosphorus (P) use efficiency (PUE) (assimilated by fish and plants, 65.0%) suggesting that lower pH and organic carbonaddition could facilitate P recovery from wastewater. </p> <p>Reverse osmosis (RO) water enables aquaculture to expand in places where natural water is not desirable and reduces uncertainty in the operation. However, high K+environment of RO in  aquaponics  couldinduce  physiological  stress,  but  adaptation  mechanism  is  unknown. Proteomic analysis revealed up-regulation of stress response proteins and down-regulation of V-type H+-ATPase and other ion transporters, suggesting cellular adaptation of fish to RO water stress. In conclusion, fish was able to accommodate to the RO environment and the benefits of efficient ammonia excretion and increased feed consumption outweighed the stress caused by RO. RO water could be a standardized water source for better animal welfare, reduce uncertainty in production and assist scaling up aquaponics industry.</p>

The olfactory system is believed to be the oldest sensory system. It developed to detect and analyse chemical information in the form of odours, and its organisation follows the same principles in almost all living animals - insects as well as mammals. Likely, the similarities are due to parallel evolution - the same type of organisation has arisen more than once. Therefore, the olfactory system is often assumed to be close to optimally designed for its tasks. Paradoxically, the workings of the olfactory system are not yet well known, although several milestone discoveries have been made during the last decades. The most well-known is probably the disovery of the olfactory receptor gene family, announced in 1991 by Linda Buck and Richard Axel. For this and subsequent work, they were awarded a Nobel Prize Award in 2004. This achievement has been of immense value for both experimentalists and theorists, and forms the basis of the current understanding of olfaction. The olfactory system has long been a focus for scientific interest, both experimental and theoretical. Ever since the field of computational neuroscience was founded, the functions of the olfactory system have been investigated through computational modelling. In this thesis, I present the basis of a biologically realistic model of the olfactory system. Our goal is to be able to represent the whole olfactory system. We are not there yet, but we have some of the necessary building blocks; a model of the input from the olfactory receptor neuron population and a model of the olfactory bulb. Taking into account the reported variability of geometrical, electrical and receptor-dependent neuronal characteristics, we have been able to model the frequency response of a population of olfactory receptor neurons. By constructing several olfactory bulb models of different size, we have shown that the size of the bulb network has an impact on its ability to process noisy information. We have also, through biochemical modelling, investigated the behaviour of the enzyme CaMKII which is known to be critical for early olfactory adaptation (suppression of constant odour stimuli). / Luktsystemet anses allmänt vara det äldsta sensoriska systemet. Det utvecklades för att upptäcka och analysera kemisk information i form av lukter, och det är organiserat efter samma principer hos nästan alla djurarter: insekter så väl som däggdjur. Troligen beror likheterna på parallell evolution -- samma organisation har uppstått mer än en gång. Därför antas det ofta att luktsystemet är nära optimalt anpassat för sina arbetsuppgifter. Paradoxalt nog är luktsystemets arbetssätt ännu inte väl känt, även om flera banbrytande framsteg gjorts de senaste decennierna. Det mest välkända är nog upptäckten av genfamiljen av luktreceptorer, som tillkännagavs 1991 av Linda Buck och Rikard Axel. För detta och efterföljande arbete belönades de med Nobelpriset år 2004. Upptäckten har varit mycket värdefull för både experimentalister och teoretiker, och formar grunden för vår nuvarande förståelse av luktsystemet. Luktsystemet har länge varit ett fokus för vetenskapligt intresse, både experimentellt och teoretiskt. Ända sedan fältet beräkningsbiologi grundades har luktsystemet undersökts genom datormodellering. I denna avhandling presenterar jag grunden för en biologiskt realistisk modell av luktsystemet. Vårt mål är att kunna representera hela luktsystemet. Så långt har vi ännu inte nått, men vi har några av de nödvändiga byggstenarna: en modell av signalerna från populationen av luktreceptorceller, och en modell av luktbulben. Genom att ta hänsyn till nervcellernas rapporterade variationer i geometriska, elektriska och receptor-beroende karaktärsdrag har vi lyckats modellera svarsfrekvenserna från en population av luktreceptorceller. Genom att konstruera flera olika stora modeller av luktbulben har vi visat att storleken på luktbulbens cellnätverk påverkar dess förmåga att behandla brusig information. Vi har också, genom biokemisk modellering, undersökt beteendet hos enzymet CaMKII, som är kritiskt viktigt för adaptering (undertryckning av ständigt närvarande luktstimuli) i luktsystemet.

This research conducted equilibrium experiments to determine ion exchange equilibria data for the inorganic cations Ca2+, Na+, and NH4+ for binary cation exchange involving sulfonic acid, polystyrene gel resins saturated with Na+ or NH4+. A linear least-square fitting was developed to find representative ion exchange capacity (IEC) and selectivity coefficient (K) values. Equilibrium experiments were utilized to test the developed new linearization method for binary systems: Ca-NH4; Ca-Na; and Na-NH4 using three commercial strong acid cation (SAC) exchange resins. It was determined that SAC exchange resins saturated with NH4+ were more selective towards Ca2+ than resins saturated with Na+. The valency and the size of the hydrated radius of the counterion influenced the selectivity of binary systems. A higher valence and a smaller hydrated radius resulted in an increased affinity of the resin for ions. Results can be used to estimate the technical and economic feasibility of a design process along with the estimation of the effect of a change in operating conditions. In addition, the removal of toxic heavy metals was also investigated with an initial metal concentration of 0.1 mg/L. Results showed that the maximum percent removal of toxic heavy metal ions, Cr3+, Pb2+, Ba2+, and Cd2+ ranged from ~ 95-99% when present in a solution containing a high molar concentration of Ca2+, Na+, and NH4+. It was observed that SAC exchange resins can effectively remove toxic heavy metals at very low concentrations. The high selectivity that SAC exchange resins possess towards heavy metals proves that they can be used as a pretreatment method for the removal of toxic heavy metals from municipal and industrial wastewaters. Moreover, the performance of SAC exchange resins for the removal of Ca2+ from waste solutions was investigated through computer modelling. Results showed that ion exchange is an efficient method for the removal of Ca2+. A sensitivity analysis showed that the variation in K and IEC greatly influenced the breakthrough time as an increase in both parameters resulted in greater Ca2+ uptake. Modelling results can be used to optimize the design of ion exchange systems for the pretreatment of inorganic cations which can reduce membrane scaling. / Thesis / Master of Applied Science (MASc)

Human mobility is a complex phenomenon emerging from the nexus between social, demographic, economic, political and environmental systems. In this thesis we develop novel mathematical models for the study of complex systems, to improve our understanding of mobility patterns and enhance our ability to predict local and global flows for real-world applications.The first and second chapters introduce the concept of human mobility from the point of view of complex systems science, showing the relation between human movements and their predominant drivers. In the second chapter in particular, we will illustrate the state of the art and a summary of our scientific contributions. The rest of the thesis is divided into three parts: structure, causes and effects.The third chapter is about the structure of a complex system: it represents our methodological contribution to Network Science, and in particular to the problem of network reconstruction and topological analysis. We propose a novel methodological framework for the definition of the topological descriptors of a complex network, when the underlying structure is uncertain. The most used topological descriptors are redefined – even at the level of a single node – as probability distributions, thus eluding the reconstruction phase. With this work we have provided a new approach to study the topological characteristics of complex networks from a probabilistic perspective. The forth chapter deals with the effects of human mobility: it represents our scientific contribution to the debate about the COVID-19 pandemic and its consequences. We present a complex-causal analysis to investigate the relationship between environmental conditions and human activity, considered as the components of a complex socio-environmental system. In particular, we derive the network of relations between different flavors of human mobility data and other social and environmental variables. Moreover, we studied the effects of the restrictions imposed on human mobility – and human activities in general – on the environmental system. Our results highlight a statistically significant qualitative improvement in the environmental variable of interest, but this improvement was not caused solely by the restrictions due to COVID-19 pandemic, such as the lockdown.The fifth and sixth chapters deal with the modelling of causes of human mobility: the former is a concise chapter that illustrate the phenomenon of human displacements caused by environmental disasters. Specifically, we analysed data from different sources to understand the factors involved in shaping mobility patterns after tropical cyclones. The latter presents the Feature-Enriched Radiation Model (FERM), our generalization of the Radiation Model which is a state-of-the-art mathematical model for human mobility. While the original Radiation Model considers only the population as a proxy for mobility drivers, the FERM can handle any type of exogenous information that is used to define the attractiveness of different geographical locations. The model exploits this information to divert the mobility flows towards the most attractive locations, balancing the role of the population distribution. The mobility patterns at different scales can be reshaped, following the exogenous drivers encoded in the features, without neglecting the global configuration of the system.

Yes / This paper presents a novel minimum entropy filter design for a class of stochastic nonlinear systems which are subjected to non-Gaussian noises. Motivated by stochastic distribution control, an output entropy model is developed using RBF neural network while the parameters of the model can be identified by the collected data. Based upon the presented model, the filtering problem has been investigated while the system dynamics have been represented. As the model output is the entropy of the estimation error, the optimal nonlinear filter is obtained based on the Lyapunov design which makes the model output minimum. Moreover, the entropy assignment problem has been discussed as an extension of the presented approach. To verify the presented design procedure, a numerical example is given which illustrates the effectiveness of the presented algorithm. The contributions of this paper can be included as 1) an output entropy model is presented using neural network; 2) a nonlinear filter design algorithm is developed as the main result and 3) a solution of entropy assignment problem is obtained which is an extension of the presented framework.

Yes / Multieffect distillation with thermal vapour compression (MED–TVC) is one of the most attractive thermal desalination technologies for the production of freshwater. Several mathematical models were presented in the open literature to analyse the steady-state performance of such process. However, these models have several limitations and assumptions. Therefore, there remains the challenge of having a reliable model to accurately predict the performance of the MED process. Thus, this research attempts to resolve this challenge by rectifying the shortcomings of the models found in the literature and create a new one. The robustness of the developed model is evaluated against the actual data of Umm Al-Nar commercial plant situated in UAE. In seawater desalinisation, a large amount of high-salinity stream (brine) is rejected back into the sea. This paper investigates the influence of the rejected (exit) brine temperature on the system performance parameters of MED–TVC process. Specifically, these parameters are considered as total heat consumption, gain output ratio, freshwater production, heat transfer area and performance ratio. Also, the particular parameters of TVC section of the entrainment ratio, compression ratio and expansion ratio are also addressed. Moreover, a critical evaluation of the influence of the rejected brine temperature on the seawater is also embedded.

yes / This work outlines a novel approach for determining thermal contact resistance (TCR) in micromoulding. The proposed technique aims to produce TCR predictions with known confidence values and combines experimental evidence (temperature fields and contact angle measurements) with various mathematical modelling procedures (parametric representation of surfaces, finite element analysis and stochastic processes). Here, emphasis is made on the mathematical aspects of the project. In particular, we focus on the description of the parametric surface representation technique based on the use of partial differential equations, known as the PDE method, which will be responsible for characterizing and compressing micro features in either moulds or surface tools. / EPSRC